The invention relates to a method for recognizing a pattern that comprises a set of physical stimuli, said method comprising the steps of:                providing a set of training observations and through applying a plurality of association models ascertaining various measuring values pj(k|x), j=1 . . . M, that each pertain to assigning a particular training observation to one or more associated pattern classes;        setting up a log/linear association distribution by combining all association models of the plurality according to respective weight factors, and joining thereto a normalization quantity to produce a compound association distribution.        
The invention has been conceived for speech recognition, but is likewise applicable to other recognition processes, such as for speech understanding, speech translation, as well as for recognizing handwriting, faces, scene recognition, and other environments. The association models may be probability models that give probability distributions for assigning patterns to classes. Other models can be based on fuzzy logic, or similarity measures, such as distances measured between target and class. Known technology has used different such models in a combined recognition attack, but the influences lent to the various cooperating models were determined in a haphazard manner. This meant that only few and/or only elementary models were feasible.
The present inventor has recognized that the unification of Maximum-Entropy and Discriminative Training principles would in case of combination of more than one model in principle be able to attain superior results as compared with earlier heuristic methods. Also, a straightforward data processing procedure should provide a cheap and fast road to those results.
In consequence, amongst other things, it is an object of the invention to evaluate a log-linear combination of various ‘sub’models pj(k|X) whilst executing parameter evaluation through discriminative training. Now, according to one of its aspects, the invention attains the object by recognizing a pattern that comprises a set of physical stimuli, said method comprising the steps of:                providing a set of training observations and through applying a plurality of association models ascertaining various measuring values pj(k|x), j=1 . . . M, that each pertain to assigning a particular training observation to one or more associated pattern classes;        setting up a log/linear association distribution by combining all association models of the plurality according to respective weight factors, and joining thereto a normalization quantity to produce a compound association distribution;        optimizing said weight factors for thereby minimizing a detected error rate of the actual assigning to said compound distribution;        recognizing target observations representing a target pattern with the help of said compound distribution. Inter alia, such procedure allows to combine any number of models into a single maximum-entropy distribution. Furthermore, it allows an optimized interaction of models that may vary widely in character and representation.        
The invention also relates to a method for modelling an association distribution according to the invention. This provides an excellent tool for subsequent users of the compound distribution for recognizing appropriate patterns.
The invention also relates to a method for recognizing patterns using a compound distribution produced by the invention. This method has users benefitting to a great deal by applying the tool realized by the invention.
The invention relates to a system that is arranged for practising a method according to the invention. Further aspects are recited in dependent Claims.